We have developed a streamlined proteomic sample preparation protocol termed Accelerated Barocycler Lysis and Extraction (ABLE), that substantially reduces the time and cost of tissue sample processing. ABLE is based on pressure cycling technology (PCT) for rapid tissue solubilisation and reliable, controlled proteolytic digestion. Here, a previously reported PCT based protocol was optimised using 1-4 mg biopsy punches from rat kidney. The tissue denaturant urea was substituted with a combination of sodium deoxycholate (SDC) and N-propanol. ABLE produced comparable numbers of protein identifications in half the sample preparation time, being ready for MS injection in three hours compared with six hours for the conventional urea based method. To validate ABLE, it was applied to a diverse range of rat tissues (kidney, lung, muscle, brain, testis), human HEK 293 cell lines and human ovarian cancer samples, followed by SWATH-mass spectrometry (SWATH-MS). There were similar numbers of quantified proteins between ABLE-SWATH and the conventional method, with greater than 70% overlap for all sample types, except muscle (58%). The ABLE protocol offers a standardised, high-throughput, efficient and reproducible proteomic preparation method, that when coupled with SWATH-MS, has the potential to accelerate proteomics analysis to achieve a clinically relevant turn-around-time.

A recent publication in this journal reported the application of a targeted proteomic strategy using a quantitative concatemer (QconCAT) standard to the assessment of allele-specific expression of UGT2B15 claiming this methodology to be a “novel” approach (J. Proteome Res. 2018, Doi: acs.jproteome.8b00620). While the application is not common, the method has previously been described and reported by our group, in relation to the quantification of CYP2B6 alleles, (J. Proteome Res. 2013, 12, 5934-42) to assess the expression of a prevalent polymorphism in a Caucasian population.

The quality of tea is highly related with the maturity of fresh tea leaf at harvest. The present study investigated the proteomic and transcriptomic profiles of tea leaves with different maturity, using iTRAQ and RNA-seq technologies. A total of 4455 proteins and 27,930 unigenes were identified, with functional enrichment analyses of GO categorization and KEGG annotation. The compositions of flavonoids (catechins and flavonols) in tea leaves were determined. The total content of flavonoids decreased with leaf maturity, in accordance with the protein regulation patterns of shikimate, phenylpropanoid and flavonoid pathways. The abundance of ANR had a positive correlation with epi-catechin content while LAR abundance was positively related with catechin content (P<0.05). The biosynthetic network of flavonoid biosynthesis was discussed in combination with photosynthesis, primary metabolism and transcription factors. Bud had the lowest activities of photosynthesis and carbon fixation but the highest flavonoid biosynthesis ability in opposite to mature leaf. SUS-INV switch might be an important joint for carbon flow shifting into the follow-up biochemical syntheses. This work provided a comprehensive overview on the functional protein profile changes of tea leaves at different growing stages and also proposed a research direction regarding the correlations between primary metabolism and flavonoid biosynthesis.

We present EBprotV2, a Perseus plug-in for peptide ratio-based differential protein expression analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially expressed proteins and non-differentially expressed proteins and computes the probability score of differential expression for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible and its R implementation is time consuming for datasets containing a large number of peptides (e.g. >100,000). The new tool built in C++ language is not only faster in computation time, but also equipped with a flexible semi-parametric model that handles skewed ratio distribution better. We also developed a Perseus plug-in for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential expression analysis using the mixture modeling. This approach is especially useful when the label-free data has many missing peptide intensity data points.

Inflammation is the first line defense mechanism against infection, tissue damage or cancer development. However, inappropriate inflammatory response may also trigger diseases. The quantification of inflammatory proteins is essential to distinguish between harmful and beneficial immune response. Currently used immunoanalytical assays may suffer specificity issues due to antigen–antibody interaction and possible cross-reactivity of antibody with other protein species. In addition, immunoanalytical assays typically require invasive blood sampling and additional logistics; they are relatively costly and highly challenging to multiplex. We present a multiplex assay based on selected reaction monitoring (SRM) for quantification of seven acute phase proteins (i.e. SAA1, SAA2-isoform1, SAA4, CRP, A1AT isoform1, A1AG1, A1AG2) and the adaptive immunity effector IGHA1 in dried blood spots (DBS). This type of samples is readily available from all human subjects including newborns. The study utilizes proteotypic isotopically labeled peptides with trypsin-cleavable tag and presents optimized and reproducible workflow and several important practical remarks regarding quantitative SRM assays development. The panel of inflammatory proteins was quantified with sequence specificity capable to differentiate protein isoforms with intra- and inter-day precision (<16.4% CV; <14.3% CV respectively). Quantitative results were correlated with immunonephelometric assay (typically >0.9 Pearson's R).

A relatively novel activation technique, EThcD was used in the LC-MS/MS analysis of tryptic glycopeptides enriched with wheat germ agglutinin from human urine samples. We focused on the characterization of mucin-type O-glycopeptides. EThcD in a single spectrum provided information on both the peptide modified and the glycan carried. Unexpectedly, glycan oxonium ions indicated the presence of O-acetyl, and even O-diacetyl-sialic acids. B and Y fragment ions revealed that i) in core 1 structures the Gal residue featured the O-acetyl-sialic acid, when there was only one in the glycan; ii) several glycopeptides featured core 1 glycans with disialic acids, in certain instances O-acetylated; iii) the disialic acid was linked to the GalNAc residue whatever was the degree of O-acetylation; iv) core 2 isomers with a single O-acetyl-sialic acid were chromatographically resolved. Glycan fragmentation also helped to decipher additional core 2 oligosaccharides: a LacdiNAc-like structure, glycans carrying sialyl LewisX/A at different stages of O-acetylation, and blood antigens. A sialo core 3 structure was also identified. We believe this is the first study when such structures were characterized from a very complex mixture and were linked not only to a specific protein, but also the sites of modifications have been determined.

We have performed deep proteomic profiling down to as few as 9 Panc-1 cells using sample fractionation, TMT multiplexing and a carrier/reference strategy. Off line fractionation of the TMT-labeled sample pooled with TMT-labeled carrier Panc-1 whole cell proteome was achieved using alkaline reversed phase spin columns. The fractionation in conjunction with the carrier/reference (C/R) proteome allowed us to detect 47,414 unique peptides derived from 6,261 proteins which provided a sufficient coverage to search for single amino acid variants (SAAVs) related to cancer. This high sample coverage is essential in order to detect a significant number of SAAVs. In order to verify genuine SAAVs versus false SAAVs, we used the SAVControl pipeline and found a total of 79 SAAVs from the 9-cell Panc-1 sample and 174 SAAVs from the 5000-cell Panc-1 C/R proteome. The SAAVs as sorted into high confidence and low confidence SAAVs were checked manually. All the high confidence SAAVs were found to be genuine SAAVs while half of the low confidence SAAVs were found to be false SAAVs mainly related to PTMs. We identified several cancer-related SAAVs including KRAS which is an important oncoprotein in pancreatic cancer. In addition, we were able to detect sites involved in loss or gain of glycosylation due to the enhanced coverage available in these experiments where we can detect both sites of loss and gain of glycosylation

Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modelling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations, and exploits flux analyses techniques to identify activated metabolic functions. Compared to usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype, rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.

Quantitative proteomics has been extensively applied in the screening of differentially regulated proteins in various research areas for decades, but its sensitivity and accuracy have been a bottleneck for many applications. Every step in the proteomics workflow can potentially affect the quantification of low-abundance proteins, but a systematic evaluation of their effects has not been done yet. In this work, to improve the sensitivity and accuracy of label-free quantification and tandem mass tags (TMT) labeling in quantifying low-abundance proteins, multi-parameter optimization was carried out using a complex 2-proteome artificial sample mixture for a series of steps from sample preparation to data analysis, including the desalting of peptides, peptide injection amount for LC-MS/MS, MS1 resolution, the length of LC-MS/MS gradient, AGC targets, ion accumulation time, MS2 resolution, precursor co-isolation threshold, data analysis software, statistical calculation methods and protein fold changes, and the best settings for each parameter were defined. The suitable cutoffs for detecting low-abundance proteins with at least 1.5-fold and 2-fold changes were identified for label-free and TMT methods, respectively. The use of optimized parameters will significantly improve the overall performance of quantitative proteomics in quantifying low-abundance proteins, and thus promote its application in other research areas.

Currently, great interest is paid to the identification of "missing" proteins that have not been detected in any biological material at the protein level (PE1). In this paper, using the UPS1 and UPS2 sets as an example, we characterized mass spectrometric approaches from the point of view of sensitivity (Sn), specificity (Sp) and accuracy (Ac). This sets consists of 48 high purity human proteins without SAP or PTM. UPS1 set consists of the same 48 proteins at 5 pmols each, in UPS2 proteins were grouped into five groups in accordance with their molar concentration, ranging from 10-11 M to 10-6 M. Ninety-two and ninety-six percent of all set proteins could be detected in a pure solution of UPS2 and UPS1, respectively, by Selected reaction monitoring with stable isotope-labeled standards (SRM SIS). We also found that in the presence of a biological matrix such as E.coli extract or human blood plasma (HBP), SRM SIS makes it possible to detect from 63% to 79% of proteins of the UPS2 set (sensitivity), with the highest specificity (~100%) and an accuracy of 80%. To increase the sensitivity of shotgun and selected reaction monitoring combined with stable isotope-labeled peptide standard (SRM SIS technology) by fractionating samples using RP chromatography under alkaline condition (2D-LC_alk). It is shown that this technique of sample fractionation allows the SRM SIS to detect 98% of the proteins present in the pure solution of UPS2 (47 out of 48 proteins). When the extracts of E-coli or P. Pastoris are added as biological matrixes to the UPS2, 46 and 45 out of 48 proteins (~95%) can be detected respectively using the SRM SIS with combined with 2D-LC_alk. The combination of the 2D-LC_alk SRM SIS and shotgun technologies allows to increase the sensitivity up to 100% in case of the proteins of UPS2. The usage of that technology can be a solution for identifying the so-called "missing" proteins and, eventually, creating the deep proteome of a particular chromosome of tissue or organs. Data in PASSEL PASS01192.

Stressful events promote psychopathogenic changes that might contribute to the development of mental illnesses. There are some individuals tend to recover from the stress response or some are not. However, the molecular mechanisms of stress resilience during stress are not well characterized. Here, we identify proteomic changes in the hippocampus by using proteomic technique to examine mice following chronic social defeat stress. We showed that small ubiquitin-like modifier (SUMO)-1 expression was significantly decreased in susceptible mice following chronic social defeat stress. We also examined protein inhibitor of activated signal transducer of transcription (PIAS)1 levels, an E3 SUMO-protein ligase protein inhibitor of activated STAT1, which is known to interact with SUMO-1. PIAS1 were shown to be profoundly decreased and monoamine oxidase (MAO)-A increased in the hippocampus of susceptible mice following chronic social defeat stress. Furthermore, manipulated PIAS1 expression in hippocampus also have an influence on glucocorticoid receptor (GR) translocation. We also found that knockdown of PIAS1 expression in hippocampus then subject to submaximal stress increased GR to GRE-binding site on the MAO-A promoter. The present study raises the possibility that differs level of PIAS1 between individuals in response to chronic social defeat stress, and that such differences may contribute to the susceptiblity to stress.